Machine Learning Engineer

Role Overview

We are looking for a mid-to-senior level ML Engineer who prioritizes "core" machine learning and statistical problem-solving. While Generative AI is part of our future, this role is 70% Classical ML and 30% GenAI . You will be responsible for building, deploying, and monitoring models that drive internal sales forecasting and customer-facing intelligence.

The ideal candidate has not "lost touch" with the fundamentals of statistics and bagging/boosting algorithms while staying current with LLM orchestration.

Key Responsibilities

• Model Development: Design and implement high-performance models using classical ML (70%) and Generative AI (30%).
• Forecasting & Optimization: Solve business-critical problems related to sales forecasting, recommendation engines, and classification.
• End-to-End MLOps: Take ownership of the full lifecycle—from data cleaning and feature engineering to deployment, hyperparameter tuning, and model monitoring.
• Productionalization: Build and maintain ML pipelines and infrastructure on cloud platforms (AWS/Azure/GCP).
• Collaboration: Work closely with the CTO and engineering teams to integrate AI agents and predictive models into a production environment.

Technical Requirements

• Core ML Expertise: Deep proficiency in algorithms such as XGBoost, Logistic Regression, Decision Trees , and various Bagging/Boosting techniques.
• Advanced Python: Strong coding skills with a focus on production-grade ML libraries (Scikit-learn, Pandas, NumPy).
• MLOps & Cloud: Hands-on experience with AWS SageMaker (or equivalent in Azure/GCP) and model monitoring tools.
• Generative AI: Experience with LLMs, Prompt Engineering, and frameworks like LangChain or similar orchestration tools.
• Statistical Foundation: Strong ability to solve complex business problems using first-principles statistics.
• Education: Background in Computer Science, Statistics, or a related field with a focus on large-scale systems.

Interview Process

1. Technical Round 1: Deep dive into ML fundamentals and project architecture.
2. Technical Round 2: Evaluation of ML theory (30%), Live Coding (30%), Project Experience (30%), and Cultural Fit (10%).
3. Final Round: Project discussion and vision alignment with the CTO.

Why Join Us?

• High Impact: Build models that directly influence business outcomes and revenue forecasting.
• Modern Stack: Work at the intersection of proven classical ML and cutting-edge GenAI.
• Ownership: Drive your own work independently in a fast-paced, high-growth environment.
• Competitive Rewards: High-percentile market salary and equity opportunities based on interview performance.